Work

sbc — Snow-influenced streamflow Bias Correction

Year: 2026

Type: software

Abstract: An explainable deep-ensemble framework for multi-scale (decadal and daily) bias correction of the GloFAS-ERA5 global river-discharge reanalysis across the snow- and glacier-influenced transboundary headwater basins of Central Asia. Bias correction is cast as log-residual learning; a gradient-boosting trio, an entity-aware LSTM and a regime-aware probabilistic flagship are combined through stacking, evaluated with a leakage-safe temporal / leave-one-basin-out / prediction-in-ungauged-regions matrix, and explained with SHAP attributions tied to snow, glacier, temperature and precipitation drivers. Supported by grant BR24993128.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21063832

Authors: Honcharenko, Tetyana, Dolhopolov, Serhii, Neftissov, Alexandr, Kazambayev, Ilyas, Kirichenko, Lalita, Kuchanskyi, Oleksandr

Institutions: Kyiv National University of Construction and Architecture, Astana IT University, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Language: English

Topic: Hydrology and Watershed Management Studies

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W7166684472

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